spatial data

types of spatial data

  • “space” is generally 2D (\(\mathbb{R}^2\); could be surface of a sphere)
  • also: networks, trees, lattices, …
  • features: points, polygons, lines (and collections thereof); rasters
  • also: continuous or categorical values associated with features
    • counts (disease incidence) or continuous values (GDP) associated with polygons (countries/provinces/counties)
    • values associated with grid cells (digital elevation models)
    • points (locations of murders)

transformations/summarization

e.g. 

  • points to density fields (2D kernel density estimation)
  • points to polygon values (square or hex binning)
  • fields to polygons (contouring)
  • point values to fields (interpolation; akima does bicubic/bilinear)

typical plots

spatial data challenges

primary packages for spatial data manipulation

  • sf (“simple features”): tidy spatial data (web page)
  • maptools

spatial plotting challenges

  • top of Cleveland hierarchy (x,y coordinates) are used up
  • insets (Alaska/Hawaii etc.)
  • map decoration
  • representing uncertainties: @correll_value-suppressing_2018, @maceachren_visualizing_2005, @koo_geovisualizing_2018
  • not misrepresenting areas (e.g. cartograms: @perrier_topogram_2019, @hohle_cartograms_2016)
  • linking?

primary R packages

  • maps (base-R maps, some basic spatial data sets)
  • ggmap (maps in ggplot, including downloading data from google maps etc.)
  • leaflet (map widget)
  • tmap (an alternative ggplot-like approach: see here)

to do